Apparatus for generating a resource probability model

ABSTRACT

In an aspect, an apparatus for generating a resource enhancement probability model is presented. An apparatus may include at least a processor and a memory communicatively connected to the at least a processor. At least a processor may be configured to receive resource data. At least a processor may be configured to compare resource data to a resource enhancement metric. At least a processor may generate a resource enhancement probability model as a function of a comparison. At least a processor may calculate, as a function of a resource enhancement probability model, a resource enchantment probability.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application Ser. No. 63,302,594, filed on Jan. 25, 2022, andtitled “PLATFORM FOR TEMPORARY AND INTERMITTENT TRANSFERS”, which isincorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of resources. Inparticular, the present invention is directed to an apparatus and methodfor generating a resource enhancement probability model.

BACKGROUND

Understanding balances of assets and trends in asset transfers can be adaunting task. There can be many factors to consider in determining asafe investment of an individual's assets.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for generating a resource enhancementprobability model is presented. An apparatus may include at least aprocessor and a memory communicatively connected to the at least aprocessor. At least a processor may be configured to receive resourcedata. At least a processor may be configured to compare resource data toa resource enhancement metric. At least a processor may generate aresource enhancement probability model as a function of a comparison. Atleast a processor may calculate, as a function of a resource enhancementprobability model, a resource enchantment probability.

In another aspect, a method of resource enhancement is presented. Amethod includes receiving resource data. A method includes comparingresource data to a resource enhancement metric. A method includesgenerating, as a function of a comparison, a resource enhancementprobability model. A method includes calculating, as a function of aresource enhancement probability model, a resource enhancementprobability.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of an apparatus forresource enhancement;

FIG. 2 is an exemplary embodiment of a fuzzy logic comparison;

FIG. 3 is an exemplary embodiment of an immutable sequential listing;

FIG. 4 is an exemplary embodiment of a machine learning model;

FIG. 5 is an exemplary embodiment of a method of enhancing resources;and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatuses and methods for generating resource enhancement probabilitymodels. In an embodiment, an apparatus for generating a resourceenhancement probability model may be configured to generate a resourceenhancement probability.

Aspects of the present disclosure can be used to provide a user withinformed recommendations of reallocation of assets. Aspects of thepresent disclosure can also be used to more accurately predict resourceenhancement probabilities. This is so, at least in part, because anapparatus for resource enhancement may utilize a resource enhancementprobability model.

Aspects of the present disclosure allow for calculating resourceenhancement probabilities. Exemplary embodiments illustrating aspects ofthe present disclosure are described below in the context of severalspecific examples.

Referring now to FIG. 1 , an exemplary embodiment of an apparatus 100for generating a resource enhancement probability model is illustrated.In some embodiments, apparatus 100 may include at least a processor anda memory communicatively connected to the at least a processor. As usedin this disclosure, “communicatively connected” means connected by wayof a connection, attachment or linkage between two or more relata whichallows for reception and/or transmittance of information therebetween.For example, and without limitation, this connection may be wired orwireless, direct or indirect, and between two or more components,circuits, devices, systems, and the like, which allows for receptionand/or transmittance of data and/or signal(s) therebetween. Data and/orsignals therebetween may include, without limitation, electrical,electromagnetic, magnetic, video, audio, radio and microwave data and/orsignals, combinations thereof, and the like, among others. Acommunicative connection may be achieved, for example and withoutlimitation, through wired or wireless electronic, digital or analog,communication, either directly or by way of one or more interveningdevices or components. Further, communicative connection may includeelectrically coupling or connecting at least an output of one device,component, or circuit to at least an input of another device, component,or circuit. For example, and without limitation, via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may also include indirect connections via, forexample and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure. A memory mayinclude instructions configuring at least a processor to perform varioustasks.

Still referring to FIG. 1 , in some embodiments, apparatus 100 mayinclude a computing device. Apparatus 100 may include any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Apparatus 100may include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Apparatus 100 may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Apparatus100 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting apparatus 100 toone or more of a variety of networks, and one or more devices. Examplesof a network interface device include, but are not limited to, a networkinterface card (e.g., a mobile network interface card, a LAN card), amodem, and any combination thereof. Examples of a network include, butare not limited to, a wide area network (e.g., the Internet, anenterprise network), a local area network (e.g., a network associatedwith an office, a building, a campus or other relatively smallgeographic space), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Apparatus 100 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Apparatus 100 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Apparatus 100 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Apparatus 100 may be implemented usinga “shared nothing” architecture in which data is cached at the worker,in an embodiment, this may enable scalability of apparatus 100 and/or acomputing device.

With continued reference to FIG. 1 , apparatus 100 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, apparatus 100 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Apparatus 100 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Continuing to refer to FIG. 1 , in some embodiments, apparatus 100 maybe configured to receive resource data 104. “Resource data” as used inthis disclosure is information pertaining to assets. “Assets” as used inthis disclosure are objects and/or entities having a value associatedtherewith. Assets may include, but are not limited to, vehicles, houses,employees, clothing, art pieces, and the like. In some embodiments,resource data 104 may include data of one or more value quantifiers. A“value quantifier” as used in this disclosure is an object correspondingto a metric of quantity. A value quantifier may include, but is notlimited to, fiat currencies, cryptocurrencies, and the like. Resourcedata 104 may include, without limitation, currency amounts, currencytransfers, income, bills, and the like.

Still referring to FIG. 1 , in some embodiments, apparatus 100 may beconfigured to generate and/or communicate with host platform 128. A“host platform” as used in this disclosure is a network-based portalenabling one or more users to interact among one another. Host platform128 may include, but is not limited to, a web portal, cloud platform,mobile application, distributed database, and/or other forms of hostplatforms. In some embodiments, host platform 128 may be downloadablefrom an application marketplace to a mobile phone, tablet, laptop,smart-wearable, personal computer, server, and the like. An applicationof host platform 128 may include a front-end client. A “front-endclient” as used in this disclosure is one or more processes thatinteract with a user. Front-end clients may include, without limitation,one or more graphical user interfaces (GUI). A “graphical userinterface” as used in this disclosure is an interface including a set ofone or more pictorial and/or graphical icons corresponding to one ormore computer actions. A GUI may be configured to receive user input, asdescribed above. A GUI may include one or more event handlers. An “eventhandler” as used in this disclosure is a callback routine that operatesasynchronously once an event takes place. Event handlers may include,without limitation, one or more programs to perform one or more actionsbased on user input, such as generating pop-up windows, submittingforms, changing background colors of a webpage, and the like. Eventhandlers may be programmed for specific user input, such as, but notlimited to, mouse clicks, mouse hovering, touchscreen input, keystrokes,and the like. For instance and without limitation, an event handler maybe programmed to generate a pop-up window if a user double clicks on aspecific icon. User input may include, a manipulation of computer icons,such as, but not limited to, clicking, selecting, dragging and dropping,scrolling, and the like. In some embodiments, user input may include anentry of characters and/or symbols in a user input field. A “user inputfield” as used in this disclosure is a portion of a graphical userinterface configured to receive data from an individual. A user inputfield may include, but is not limited to, text boxes, search fields,filtering fields, and the like. In some embodiments, user input mayinclude touch input. Touch input may include, but is not limited to,single taps, double taps, triple taps, long presses, swiping gestures,and the like. One of ordinary skill in the art will appreciate thevarious ways a user may interact with a GUI.

Still referring to FIG. 1 , in some embodiments, host platform 128 mayinclude one or more back-end clients. A “back-end client” as used inthis disclosure, is one or more processes that interact with datastorage and/or data retrieval. A back-end client of host platform 128may include, without limitation, one or more process that handlequeries, user input, and the like. A back-end client of host platform128 may communicate with one or more user databases, financial accounts,and the like. A “user database” as used in this disclosure is acollection of information of an individual. A “financial database” asused in this discourse is a collection of stored user data relating tocurrency. A financial database may include, without limitation, mobileapplications, banking accounts, crypto wallets, and the like. In someembodiments, a back-end client of host platform 128 may communicate witha front-end client of host platform 128 through an applicationprogramming interface (API). An “application programming interface” asused in this disclosure is software that enables two or more computersto communicate data to one another. An API may receive data from afront-end client of host platform 128 and communicate the data to aback-end client of host platform 128, and vice versa.

Still referring to FIG. 1 , in some embodiments, apparatus 100 mayreceive user data 124, such as through host platform 128, user input,and/or an external computing device, without limitation. “User data” asused in this disclosure is information pertaining to an individual. Userdata 124 may include information such as, but not limited to, employmentinformation, hobby information, health information, and the like. Userdata 124 may include, without limitation, authentication credentials,user profiles, and the like. User profiles may include, withoutlimitation, geographical data, demographic data, employment data, familydata, and the like. Apparatus 100 may generate one or more user profilesas a function of user data 124. User data 124 may include data of anorganization, such as, but not limited to, excess funds, payrollaccounts, pension accounts, and the like. In some embodiments, resourcedata 104 may be generated through apparatus 100. Apparatus 100 maygenerate resource data 104 through tracking and/or comparing user data124 over a period of time, such as, without limitation, minutes, hours,days, weeks, months, years, and the like. In other embodiments, resourcedata 104 may be received from an external computing device, such as,without limitation, a server, desktop, laptop, cloud-computing network,and the like. User data 124 may be received through a financialdatabase.

Still referring to FIG. 1 , in some embodiments, apparatus 100 mayextract resource data 104 from user data 124. Extraction may includefiltering and/or querying through user data 124 for one or morekeywords, phrases, symbols, and the like. Apparatus 100 may beconfigured to perform optical character recognition. In someembodiments, optical character recognition or optical character reader(OCR) includes automatic conversion of images of written (e.g., typed,handwritten or printed text) into machine-encoded text. In some cases,recognition of at least a keyword from an image component may includeone or more processes, including without limitation optical characterrecognition (OCR), optical word recognition, intelligent characterrecognition, intelligent word recognition, and the like. In some cases,OCR may recognize written text, one glyph or character at a time. Insome cases, optical word recognition may recognize written text, oneword at a time, for example, for languages that use a space as a worddivider. In some cases, intelligent character recognition (ICR) mayrecognize written text one glyph or character at a time, for instance byemploying machine learning processes. In some cases, intelligent wordrecognition (IWR) may recognize written text, one word at a time, forinstance by employing machine learning processes.

Still referring to FIG. 1 , in some cases OCR may be an “offline”process, which analyses a static document or image frame. In some cases,handwriting movement analysis can be used as input to handwritingrecognition. For example, instead of merely using shapes of glyphs andwords, this technique may capture motions, such as the order in whichsegments are drawn, the direction, and the pattern of putting the pendown and lifting it. This additional information can make handwritingrecognition more accurate. In some cases, this technology may bereferred to as “online” character recognition, dynamic characterrecognition, real-time character recognition, and intelligent characterrecognition.

Still referring to FIG. 1 , in some cases, OCR processes may employpre-processing of image component. Pre-processing process may includewithout limitation de-skew, de-speckle, binarization, line removal,layout analysis or “zoning,” line and word detection, scriptrecognition, character isolation or “segmentation,” and normalization.In some cases, a de-skew process may include applying a transform (e.g.,homography or affine transform) to image component to align text. Insome cases, a de-speckle process may include removing positive andnegative spots and/or smoothing edges. In some cases, a binarizationprocess may include converting an image from color or greyscale toblack-and-white (i.e., a binary image). Binarization may be performed asa simple way of separating text (or any other desired image component)from a background of image component. In some cases, binarization may berequired for example if an employed OCR algorithm only works on binaryimages. In some cases. a line removal process may include removal ofnon-glyph or non-character imagery (e.g., boxes and lines). In somecases, a layout analysis or “zoning” process may identify columns,paragraphs, captions, and the like as distinct blocks. In some cases, aline and word detection process may establish a baseline for word andcharacter shapes and separate words, if necessary. In some cases, ascript recognition process may, for example in multilingual documents,identify script allowing an appropriate OCR algorithm to be selected. Insome cases, a character isolation or “segmentation” process may separatesignal characters, for example character-based OCR algorithms. In somecases, a normalization process may normalize aspect ratio and/or scaleof image component.

Still referring to FIG. 1 , in some embodiments an OCR process willinclude an OCR algorithm. Exemplary OCR algorithms include matrixmatching process and/or feature extraction processes. Matrix matchingmay involve comparing an image to a stored glyph on a pixel-by-pixelbasis. In some case, matrix matching may also be known as “patternmatching,” “pattern recognition,” and/or “image correlation.” Matrixmatching may rely on an input glyph being correctly isolated from therest of the image component. Matrix matching may also rely on a storedglyph being in a similar font and at a same scale as input glyph. Matrixmatching may work best with typewritten text.

Still referring to FIG. 1 , in some embodiments, an OCR process mayinclude a feature extraction process. In some cases, feature extractionmay decompose a glyph into features. Exemplary non-limiting features mayinclude corners, edges, lines, closed loops, line direction, lineintersections, and the like. In some cases, feature extraction mayreduce dimensionality of representation and may make the recognitionprocess computationally more efficient. In some cases, extracted featurecan be compared with an abstract vector-like representation of acharacter, which might reduce to one or more glyph prototypes. Generaltechniques of feature detection in computer vision are applicable tothis type of OCR. In some embodiments, machine-learning process likenearest neighbor classifiers (e.g., k-nearest neighbors algorithm) canbe used to compare image features with stored glyph features and choosea nearest match. OCR may employ any machine-learning process describedin this disclosure, for example machine-learning processes describedwith reference to FIG. 6 . Exemplary non-limiting OCR software includesCuneiform and Tesseract. Cuneiform is a multi-language, open-sourceoptical character recognition system originally developed by CognitiveTechnologies of Moscow, Russia. Tesseract is free OCR softwareoriginally developed by Hewlett-Packard of Palo Alto, Calif., UnitedStates.

Still referring to FIG. 1 , in some cases, OCR may employ a two-passapproach to character recognition. Second pass may include adaptiverecognition and use letter shapes recognized with high confidence on afirst pass to recognize better remaining letters on the second pass. Insome cases, two-pass approach may be advantageous for unusual fonts orlow-quality image components where visual verbal content may bedistorted. Another exemplary OCR software tool include OCRopus. OCRopusdevelopment is led by German Research Centre for Artificial Intelligencein Kaiserslautern, Germany. In some cases, OCR software may employneural networks, for example neural networks as taught in reference toFIG. 6 .

Still referring to FIG. 1 , in some cases, OCR may includepost-processing. For example, OCR accuracy can be increased, in somecases, if output is constrained by a lexicon. A lexicon may include alist or set of words that are allowed to occur in a document. In somecases, a lexicon may include, for instance, all the words in the Englishlanguage, or a more technical lexicon for a specific field. In somecases, an output stream may be a plain text stream or file ofcharacters. In some cases, an OCR process may preserve an originallayout of visual verbal content. In some cases, near-neighbor analysiscan make use of co-occurrence frequencies to correct errors, by notingthat certain words are often seen together. For example, “Washington,D.C.” is generally far more common in English than “Washington DOC.” Insome cases, an OCR process may make us of a priori knowledge of grammarfor a language being recognized. For example, grammar rules may be usedto help determine if a word is likely to be a verb or a noun. Distanceconceptualization may be employed for recognition and classification.For example, a Levenshtein distance algorithm may be used in OCRpost-processing to further optimize results.

Still referring to FIG. 1 , in some embodiments, apparatus 100 mayextract resource data 104 from user data 124 through a languageprocessing module. A language processing module may include any hardwareand/or software module. A language processing module may be configuredto extract, from the one or more documents, one or more words. One ormore words may include, without limitation, strings of one or morecharacters, including without limitation any sequence or sequences ofletters, numbers, punctuation, diacritic marks, engineering symbols,geometric dimensioning and tolerancing (GD&T) symbols, chemical symbolsand formulas, spaces, whitespace, and other symbols, including anysymbols usable as textual data as described above. Textual data may beparsed into tokens, which may include a simple word (sequence of lettersseparated by whitespace) or more generally a sequence of characters asdescribed previously. The term “token,” as used herein, refers to anysmaller, individual groupings of text from a larger source of text;tokens may be broken up by word, pair of words, sentence, or otherdelimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

Still referring to FIG. 1 , a language processing module may operate toproduce a language processing model. A language processing model mayinclude a program automatically generated by computing device and/orlanguage processing module to produce associations between one or morewords extracted from at least a document and detect associations,including without limitation mathematical associations, between suchwords. Associations between language elements, where language elementsinclude for purposes herein extracted words, relationships of suchcategories to other such term may include, without limitation,mathematical associations, including without limitation statisticalcorrelations between any language element and any other language elementand/or language elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of semantic meaning. As a further example,statistical correlations and/or mathematical associations may includeprobabilistic formulas or relationships indicating a positive and/ornegative association between at least an extracted word and/or a givensemantic meaning; positive or negative indication may include anindication that a given document is or is not indicating a categorysemantic meaning. Whether a phrase, sentence, word, or other textualelement in a document or corpus of documents constitutes a positive ornegative indicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat computing device, or the like.

Still referring to 1, language processing module and/or diagnosticengine may generate the language processing model by any suitablemethod, including without limitation a natural language processingclassification algorithm; language processing model may include anatural language process classification model that enumerates and/orderives statistical relationships between input terms and output terms.Algorithm to generate language processing model may include a stochasticgradient descent algorithm, which may include a method that iterativelyoptimizes an objective function, such as an objective functionrepresenting a statistical estimation of relationships between terms,including relationships between input terms and output terms, in theform of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted words, phrases, and/orother semantic units. There may be a finite number of categories towhich an extracted word may pertain; an HMM inference algorithm, such asthe forward-backward algorithm or the Viterbi algorithm, may be used toestimate the most likely discrete state given a word or sequence ofwords. Language processing module may combine two or more approaches.For instance, and without limitation, machine-learning program may use acombination of Naive-Bayes (NB), Stochastic Gradient Descent (SGD), andparameter grid-searching classification techniques; the result mayinclude a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 1 , generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1 , language processing module may use a corpusof documents to generate associations between language elements in alanguage processing module, and diagnostic engine may then use suchassociations to analyze words extracted from one or more documents anddetermine that the one or more documents indicate significance of acategory. In an embodiment, language module and/or apparatus 100 mayperform this analysis using a selected set of significant documents,such as documents identified by one or more experts as representing goodinformation; experts may identify or enter such documents via graphicaluser interface, or may communicate identities of significant documentsaccording to any other suitable method of electronic communication, orby providing such identity to other persons who may enter suchidentifications into apparatus 100. Documents may be entered into acomputing device by being uploaded by an expert or other persons using,without limitation, file transfer protocol (FTP) or other suitablemethods for transmission and/or upload of documents; alternatively oradditionally, where a document is identified by a citation, a uniformresource identifier (URI), uniform resource locator (URL) or other datumpermitting unambiguous identification of the document, diagnostic enginemay automatically obtain the document using such an identifier, forinstance by submitting a request to a database or compendium ofdocuments such as JSTOR as provided by Ithaka Harbors, Inc. of New York.

Still referring to FIG. 1 , in some embodiments, apparatus 100 mayclassify resource data 104 to one or more resource categories, such as,but not limited to, unused assets, used assets, asset increasers, assetdecreases, and the like. “Unused assets” as used in this disclosure areobjects and/or items of value having a static behavior. A “staticbehavior” as used in this disclosure is an unchanging conduct of objectsand/or items. As a non-limiting example, a static behavior may include aquantity of one or more value quantifiers sitting in a savings accountfor over 6 months. Unused assets may include, without limitation,quantities of value quantifiers, real estate space, vehicles, and thelike. “Used assets” as used in this disclosure are objects and/or itemsbeing utilized by an individual and/or company. Used assets may include,without limitation, value quantifiers, real estate space, vehicles,employees, and the like. An “asset increaser” as used in this disclosureis an object and/or item that adds value to one or more resources. Assetincreasers may include, without limitation, investments, income,transactions, and the like. An “asset decreaser” as used in thisdisclosure is an object and/or item that removes value of one or moreresources. Asset decreasers may include, without limitation, real estatespaces, vehicles, value quantifiers, transactions, and the like.Apparatus 100 may utilize a resource classifier to classify resourcedata 104 to one or more resource categories. A “resource classifier” asused in this disclosure is a machine learning model that categorizesresources to one or more groups A resource classifier may be trainedwith training data correlating resource data to resource categories,such as, but not limited to, used assets, unused asset, assetincreasers, asset decreasers, and/or other resource categories. Trainingdata may be received through user input, external computing devices,and/or previous iterations of processing.

Still referring to FIG. 1 , a “classifier,” as used in this disclosureis a machine-learning model, such as a mathematical model, neural net,or program generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier may beconfigured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric as described below, or the like. Apparatus 100and/or another device may generate a classifier using a classificationalgorithm, defined as a processes whereby apparatus 100 derives aclassifier from training data. Classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 1 , apparatus 100 may be configured to generatea classifier using a Naïve Bayes classification algorithm. Naïve Bayesclassification algorithm generates classifiers by assigning class labelsto problem instances, represented as vectors of element values. Classlabels are drawn from a finite set. Naïve Bayes classification algorithmmay include generating a family of algorithms that assume that the valueof a particular element is independent of the value of any otherelement, given a class variable. Naïve Bayes classification algorithmmay be based on Bayes Theorem expressed as P(A/B)=P(B/A) P(A)÷P(B),where P(A/B) is the probability of hypothesis A given data B also knownas posterior probability; P(B/A) is the probability of data B given thatthe hypothesis A was true; P(A) is the probability of hypothesis A beingtrue regardless of data also known as prior probability of A; and P(B)is the probability of the data regardless of the hypothesis. A naïveBayes algorithm may be generated by first transforming training datainto a frequency table. Apparatus 100 may then calculate a likelihoodtable by calculating probabilities of different data entries andclassification labels. Apparatus 100 may utilize a naïve Bayes equationto calculate a posterior probability for each class. A class containingthe highest posterior probability is the outcome of prediction. NaïveBayes classification algorithm may include a gaussian model that followsa normal distribution. Naïve Bayes classification algorithm may includea multinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , apparatus 100 may be configured togenerate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)α_(i) ²)}, whereα_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

Still referring to FIG. 1 , in some embodiments, apparatus 100 maydetermine and/or receive resource enhancement metric 108. A “resourceenhancement metric” as used in this disclosure is a value and/or rangeof values indicative of asset performance. “Asset performance” as usedin this disclosure is an increase or decrease of value of one or moreassets. Resource enhancement metric 108 may include, without limitation,quantities of value quantifiers, balances of resources, increases invalue of resources, decreases of values of resources, and the like.Resource enhancement metric 108 may include a temporal element. A“temporal element” as used in this disclosure is a metric of time. Atemporal element may include, without limitation, seconds, minutes,hours, days, weeks, months, years, and the like. As a non-limitingexample, resource enhancement metric 108 may include an increase incryptocurrency assets by 10% with a temporal element of 2 years. In someembodiments, apparatus 100 may determine resource enhancement metric 108through a resource enhancement machine learning model. A resourceenhancement machine learning model may be trained with training datacorrelating resource data to resource enhancement metrics 108. Trainingdata may be received through user input, external computing devices,and/or previous iterations of processing. A resource enhancement machinelearning model may be configured to input resource data and output oneor more resource enhancement metrics 108. In some embodiments, apparatus100 may determine resource enhancement metric 108 as a function of userdata 124. Apparatus 100 may be configured to determine a user pattern ofuser data 124. A “user pattern” as used in this disclosure is arelationship between user data and one or more user categories. A userpattern may include, without limitation, trends of one or more currencydistributions, average quantities of currency distributions, frequenciesof currency distribution types, and the like. In some embodiments,apparatus 100 may utilize a user pattern machine learning model. A userpattern machine learning model may be trained with training datacorrelating user data to one or more user patterns. Training data may bereceived through user input, external computing devices, and/or previousiterations of processing. In some embodiments, a user pattern machinelearning model may be configured to input user data 124 and output userpatterns. Apparatus 100 may determine resource enhancement metric 108 asa function a user pattern. For instance and without limitation, a user apattern may show a frequent income of a quantity of 500,000 valuequantifiers every month. Apparatus 100 may determine resourceenhancement metric 108 to include a quantity of 500,000 valuequantifiers with a temporal element of one month.

Still referring to FIG. 1 , in some embodiments, apparatus 100 maycompare resource data 104 to resource enhancement metric 108. Apparatus100 may compute a score associated with resource data 104 and selectresource enhancement metrics 108 to minimize and/or maximize the score,depending on whether an optimal result is represented, respectively, bya minimal and/or maximal score; a mathematical function, describedherein as an “objective function,” may be used by apparatus 100 to scoreeach possible pairing. Objective function may be based on one or moreobjectives as described below. In various embodiments a score of aparticular resource datum of resource data 104 may be based on acombination of one or more factors, including value quantifiers,quantities of assets, and the like. Each factor may be assigned a scorebased on predetermined variables. In some embodiments, the assignedscores may be weighted or unweighted.

Still referring to FIG. 1 , optimization of an objective function mayinclude performing a greedy algorithm process. A “greedy algorithm” isdefined as an algorithm that selects locally optimal choices, which mayor may not generate a globally optimal solution. For instance, apparatus100 may select resource enhancement metrics 108 so that scoresassociated therewith are the best score for each resource data 104. Anobjective function may be formulated as a linear objective function,which apparatus 100 may solve using a linear program such as withoutlimitation a mixed-integer program. A “linear program,” as used in thisdisclosure, is a program that optimizes a linear objective function,given at least a constraint. For instance, a constraint may include avalue and/or range of values of percent increases in resource values. Invarious embodiments, apparatus 100 may determine resource enhancementmetric 108 that maximizes a total score subject to a constraint thatunused assets are utilized above a threshold value, such as 40%. Amathematical solver may be implemented to solve for the set resourceenhancement metrics 108 that maximizes scores; mathematical solver maybe implemented on apparatus 100 and/or another device, and/or may beimplemented on third-party solver.

With continued reference to FIG. 1 , optimizing objective function mayinclude minimizing a loss function, where a “loss function” is anexpression an output of which an optimization algorithm minimizes togenerate an optimal result. As a non-limiting example, apparatus 100 mayassign variables relating to a set of parameters, which may correspondto score components as described above, calculate an output ofmathematical expression using the variables, and select a resourceenhancement metric 108 that produces an output having the lowest size,according to a given definition of “size,” of the set of outputsrepresenting each of plurality of candidate ingredient combinations;size may, for instance, included absolute value, numerical size, or thelike. Selection of different loss functions may result in identificationof different potential pairings as generating minimal outputs.

Still referring to FIG. 1 , in some embodiments, apparatus 100 maydetermine a portion of assets that may be unused as a function of acomparison of resource data 104 to resource enhancement metric 108.Apparatus 100 may search through financial accounts, databases, and thelike to generate resource data 104 and determine, as a function of acomparison to resource enhancement metric 108, a quantity of unusedand/or excess assets. In some embodiments, apparatus 100 may compareresource data 104 with resource enhancement metric 108 to determine aresource variability. In other embodiments, apparatus 100 may utilizeresource enhancement probability model 116 to determine a resourcevariability. A “resource variability” as used in this disclosure is achange in value of one or more assets. A resource variability mayinclude, but is not limited to, percent increases in value, percentdecreases in value, utilization percentage, market values, and the like.As a non-limiting example, a resource variability may include anincrease in fiat currency value of one or more assets by 15% over a spanof 3 months. In some embodiments, a resource variability may include atemporal element. For instance, and without limitation, a temporalelement may include days, weeks, months, years, and the like. A resourcevariability may include a risk element of one or more resources of auser's account. A “risk element” as used in this disclosure is a metricpertaining to a loss probability of assets. A resource variability mayinclude risk elements such as, but not limited to, high-risk, low-risk,medium-risk, and the like. In some embodiments, a resource variabilitymay include a probability of missing a financial inquiry when moving oneor more resources. A “financial inquiry” as used in this disclosure is arequest for one or more value quantifiers from an entity. Financialinquires may include, without limitation, bills, pending payments, andthe like. Apparatus 100 may calculate a resource variability to includea probability of defaulting on one or more financial inquiries of auser. For instance, and without limitation, a resource variability mayinclude a 30% probability of defaulting on a $300 payment if $400 worthof assets of a user's account are removed from their savings account andare invested into the New York Stock Exchange (NYSE).

Still referring to FIG. 1 , in some embodiments, apparatus 100 maygenerate resource enhancement probability model 112. A “resourceenhancement probability model” as used in this disclosures is a machineearning process that predicts changes in assets. Resource enhancementprobability model 112 may be trained with training data correlatingresource data and/or resource enhancement metrics to resourcevariabilities. Training data may be received through user input,external computing devices, and/or previous iterations of processing.Resource enhancement probability model 112 may be configured to inputresource data and/or resource enhancement metrics and output one or moreresource variabilities. Apparatus 100 may use resource enhancementprobability model 112 to predict portions of a user's financial accountthat may be utilized, such as, without limitation, checking accounts,savings accounts, and the like. Apparatus 100 may use resourceenhancement probability model 112 to predict loss probabilities of oneor more resource categories, such as, but not limited to, used assets,unused asset, asset increasers, asset decreases, and the like. Forinstance and without limitation, apparatus 100 may predict a lossprobability of 3% for $10,000 moved from a user's savings account to aninvestment account based on a user's upcoming expenses. Resourceenhancement probability model 112 may be configured and/or trained topinpoint a quantity of value identifiers that may satisfy one or morefinancial inquiries of a user's financial accounts. For instance, andwithout limitation, resource enhancement probability model 112 maydetermine, based on a user's financial history and/or user patterns,that the user will need at least $412 on Sep. 16, 2022, to satisfy afinancial inquiry of an auto insurance company. Resource enhancementprobability model 112 may correlate and/or identify one or moremagnitudes of transactions, frequency of transactions, and the like topredict future transaction amounts, frequencies, and the like, withoutlimitation.

Still referring to FIG. 1 , in some embodiments, apparatus 100 may beconfigured to generate resource enhancement probability 116 as afunction of resource data 104, resource enhancement metric 108, and/orresource probability model 112, without limitation. A “resourceenhancement probability” as used in this disclosure is a probability ofincreasing value of one or more assets. Resource enhancement probability116 may be generated by resource probability model 112. In someembodiments, apparatus 100 may determine unused assets from resourcedata 104 and generate resource enhancement probability 116 to increasevalue of one or more assets of resource data 104. Apparatus 100 maydetermine a temporal element of one or more financial inquires, such as,but not limited to, days, weeks, months, years, and the like. Resourceenhancement probability 116 may include a cushion element. A “cushionelement” as used in this disclosure is a time period before a financialinquiry needs to be fulfilled. For instance, and without limitation, acushion element may include days, weeks, months, and the like. Apparatus100 may determine upcoming financial inquires based on user patterns, asdescribed above. In some embodiments, apparatus 100 may calculate aresource variability based on one or more financial inquiries and/orcushion elements. For instance, and without limitation, a financialinquiry three days away may increase a loss probability of utilizing oneor more assets of a user's account, whereas a financial inquiry threemonths away may lower a loss probability of utilizing one or more assetsof a user's account. Apparatus 100 may generate resource enhancementprobability 116 to include one or more transfers of assets to accountfor upcoming financial inquires. Resource enhancement probability 116may include a temporary withdrawal and investment of one or more assetsof a user's account. As a non-limiting example, apparatus 100 maydetermine user data 124 such as, but not limited to, account balancehistory information, transactional history, debits, credits, and thelike, and predict a quantity of assets that may be safely available toinvest for a few days, weeks, and the like. Resource enhancementprobability 116 may include a process of converting fiat currency of auser's accounts to cryptocurrency and invest the cryptocurrency. In someembodiments, apparatus 100 may communicate with an investment partner,which may include the investment partner staking an investment to asmart contract. Apparatus 100 may determine a risk element of a smartcontract, such as, but not limited to, erroneous code, unstable cryptowallets, anomalous wallet behavior, and the like. Apparatus 100 maywithdraw assets from a crypto wallet and/or smart contract as a functionof a risk element of a smart contract.

Still referring to FIG. 1 , in some embodiments, host platform 128 maybe configured to perform a temporary transfer and/or return of resourcesof a user's account. A “temporary transfer” as used in this disclosureis an allocation of assets that returns to an initial account within atime period. In some embodiments, host platform 128 may receive userdata 124 in a form of, but not limited to, a bank account identifier(e.g., account number, bank ID, etc.) of an account that the user wouldlike to temporarily invest from. Host platform 128 may receive anaccount identifier and identify a corresponding financial institutionthat issued the account number. Host platform 128 may access transactionhistory, account balance history, and the like of a bank account from afinancial institution, for example, and without limitation, via one ormore API calls to an API of the financial institution. An API call mayinclude an identifier of the bank account of a user. A financialinstitution may provide transaction history from a bank account over apredetermined historical period of time (e.g., 2 years, 5 years, etc.)and transmit account history information to host platform 128 inresponse to an API call.

Still referring to FIG. 1 , apparatus 100 may process and/or analyzeaccount history information of resource data 104, which may be extractedfrom user data 124 through host platform 128. Apparatus 100 may generatea resource enhancement process to provide a user with a recommendedinvestment amount and time window. In some embodiments, a user mayinclude an organization with a payroll account. As a non-limitingexample, an organization may include a payroll account where money sitsand earns 0.2% interest. In this example, apparatus 100 may utilize oneor more machine learning models to analyze the transaction history ofthe organization and provide a “safe” investment amount as well as aperiod of time in which the amount can be invested. A safe investmentamount may include a predetermined amount of fiat currency, along with acushion (e.g., a few thousand dollars, etc.) that may provide anadditional amount of protection in the account in the case that one ormore unexpected expenses occur. A cushion may be kept in a bank accountrather than being invested. For example, and without limitation, anorganization may have $1,000,000 dollars in their payroll account. Oneor more machine learning models may analyze the transaction history ofthe user including account balance history, expenses, timinginformation, etc., and identify a pattern of spending behavior and alsoa pattern of the account balance. One or more machine learning modelsmay learn that a payroll account also is used for making payments onsupplies every 3 months that can be of a significant expense (e.g.,$75,000). One or more machine learning models may also learn that thepayment for supplies is to occur in 1 week. One or more machine learningmodels may determine that $925,000 of an account balance is safe toinvest. In some embodiments, to incorporate a cushion, a machinelearning models may add in a buffer to prevent an account from beingoverdrawn. For example, and without limitation, a buffer and/or cushionmay include an amount of $25,000. Continuing this example, one or moremachine learning models 222 may recommend a total investment amount of$900,000 based on a buffer amount of $25,000.

Still referring to FIG. 1 , as another non-limiting example, apparatus100 may learn that a payroll account has a significant amount ofhistorical fluctuation during a following month, for example, because ofbonuses being paid out to employees, etc. In this case, apparatus 100and/or one or more machine learning models may recommend that a timewindow expire before a following month, which happens to be 3 weeksaway. Continuing this example, one or more machine learning models mayoutput a recommendation of investing $900,000 for a total of 18 days.This time period may be determined by the one or more machine learningmodels based on subtracting a period of time necessary to return themoney to the account (e.g., 2 days, etc.). This information can be sentby host platform 128 to a front-end of the application on a user device.A “user device” as used in this disclosure is a computing deviceoperated by an individual. A user device may include, withoutlimitation, a smartphone, laptop, tablet, desktop, server, and the like.

Referring still to FIG. 1 , in some embodiments, apparatus 100 and/orhost platform 128 may perform a process of transferring funds from abank account of a user to a crypto-investor. A “crypto-investor” as usedin this disclosure is a financial entity operating in cryptocurrency. Insome embodiments, apparatus 100 may process a transfer of funds for thepurpose of a short-term investment. As a non-limiting example, hostplatform 128 and/or apparatus 100 may act as an agent for a user andreceive an authorization from a user device to invest a predeterminedamount of money for a predetermined amount of time, such as the $900,000for 18 days, in the example given above. Host platform 128 may alsoreceive “authorization” from a user device to automatically pull moneyout of the payroll account, and return the money to the payroll accountwithin the 18 day period, or less. In response, host platform 128 maytransfer funds from the payroll account hosted by a financialinstitution to a crypto-investor. A crypto-investor may convert funds,such as fiat currency, into cryptocurrency (e.g., Bitcoin, etc.) andthen invest the cryptocurrency in any number of blockchain-basednetworks, such as stablecoin networks which may allow staking to smartcontracts. In some embodiments, in response to receiving fiat currencyfrom host platform 128, a crypto-investor may convert the fiat currencyinto cryptocurrency and store the cryptocurrency in a temporaryblockchain wallet. A crypto-investor may give both a user of a userdevice and host platform 128 access to a blockchain wallet. For example,and without limitation, both a user device and host platform 128 may begiven respective keys for accessing a blockchain wallet.

Referring still to FIG. 1 , as another non-limiting example, acrypto-investor may have a predetermined investment strategy (e.g.,submitted by a user, etc.) that may be specified ahead of time. Acrypto-investor may temporarily invest cryptocurrency in a blockchainwallet in a blockchain network until a predetermined period has expired(e.g., the 18 days are up), or until a predetermined amount of interesthas been earned (e.g., 10%, etc.) Continuing this example, eithercondition may trigger a crypto-investor (e.g., based on a request fromhost platform 128, based on an internal trigger by a crypto-investor,etc.), to pull the money out of the blockchain network and return themoney back to host platform 128. Here, a crypto-investor maytransfer/sell/exchange the cryptocurrency from a blockchain wallet via ablockchain network, and convert the resulting cryptocurrency back intofiat currency. Still continuing this example, a crypto-investor mayreturn the fiat currency back to host platform 128, which mayautomatically put the money (with the interest earned by thecrypto-investor) to the user's account at a financial institution. As aresult of the example embodiments, a traditional bank account that earnsvery little or no interest, can earn significantly more interest byperforming intermittent and continuous short-term investments based on acombination of account history analysis and temporary crypto-investmentsthat can yield substantially more interest than a traditional bankaccount. In some embodiments, one or more of host platform 128 and acrypto-investor may take a small percentage for their role in the aboveprocess.

Still referring to FIG. 1 , in some embodiments, host platform 128 mayalso provide automated “claimless” insurance for digital currency yieldaggregators. In the example embodiments, a new kind of vault (yieldaggregator) is provided that provides a negative yield relative to thepotential, but in favor, a user's principal funds may be insured. Inother words, a yield aggregator may take a percentage of anygrowth/interest earned on the principal, but the yield aggregator mayalso cover any losses to the principal funds, should an investment gobad/poorly. As a non-limiting example, instead of earning 5%, a user mayearn 85% of 5% which is equal to 4.25% of the gains instead of the whole5%. However, if the principal goes down from $100 to $80, the user maynot lose anything and the yield aggregator may lose $20.

Still referring to FIG. 1 , in some embodiments, apparatus 100 may beconfigured to display resource enhancement probability 116, resourcedata 104, and/or other data as described above, without limitation,through GUI 120. GUI 120 may include a graphical user interface asdescribed above. In some embodiments, GUI 120 may include a display of alaptop, smartphone, tablet, monitor, and the like. GUI 120 may includeone or more graphs, pictorial icons, and the like that may representresource data 104, resource variability 112, resource enhancementprobability 116, and the like. In some embodiments, GUI 120 may beconfigured to receive user input. A user may interact with one or moregraphical elements of GUI 120, such as, but not limited to, clicking,dragging, dropping, entry of one or more text fields, and the like. GUI120 may be configured to display resource data 104 as a function of userinput. As a non-limiting example, a user may click on a graphicalelement, which may trigger an event handler of the graphical elementwhich may display additional resource data 104 through GUI 120.

Referring now to FIG. 2 , an exemplary embodiment of an immutablesequential listing 200 is illustrated. An “immutable sequentiallisting,” as used in this disclosure, is a data structure that placesdata entries in a fixed sequential arrangement, such as a temporalsequence of entries and/or blocks thereof, where the sequentialarrangement, once established, cannot be altered or reordered. Animmutable sequential listing may be, include and/or implement animmutable ledger, where data entries that have been posted to theimmutable sequential listing cannot be altered. Data elements arelisting in immutable sequential listing 200; data elements may includeany form of data, including textual data, image data, encrypted data,cryptographically hashed data, and the like. Data elements may include,without limitation, one or more at least a digitally signed assertions.In one embodiment, a digitally signed assertion 204 is a collection oftextual data signed using a secure proof as described in further detailbelow; secure proof may include, without limitation, a digital signatureas described above. Collection of textual data may contain any textualdata, including without limitation American Standard Code forInformation Interchange (ASCII), Unicode, or similar computer-encodedtextual data, any alphanumeric data, punctuation, diacritical mark, orany character or other marking used in any writing system to conveyinformation, in any form, including any plaintext or cyphertext data; inan embodiment, collection of textual data may be encrypted, or may be ahash of other data, such as a root or node of a Merkle tree or hashtree, or a hash of any other information desired to be recorded in somefashion using a digitally signed assertion 204. In an embodiment,collection of textual data states that the owner of a certaintransferable item represented in a digitally signed assertion 204register is transferring that item to the owner of an address. Adigitally signed assertion 204 may be signed by a digital signaturecreated using the private key associated with the owner's public key, asdescribed above.

Still referring to FIG. 2 , a digitally signed assertion 204 maydescribe a transfer of virtual currency, such as crypto-currency asdescribed below. The virtual currency may be a digital currency. Item ofvalue may be a transfer of trust, for instance represented by astatement vouching for the identity or trustworthiness of the firstentity. Item of value may be an interest in a fungible negotiablefinancial instrument representing ownership in a public or privatecorporation, a creditor relationship with a governmental body or acorporation, rights to ownership represented by an option, derivativefinancial instrument, commodity, debt-backed security such as a bond ordebenture or other security as described in further detail below. Aresource may be a physical machine e.g. a ride share vehicle or anyother asset. A digitally signed assertion 204 may describe the transferof a physical good; for instance, a digitally signed assertion 204 maydescribe the sale of a product. In some embodiments, a transfernominally of one item may be used to represent a transfer of anotheritem; for instance, a transfer of virtual currency may be interpreted asrepresenting a transfer of an access right; conversely, where the itemnominally transferred is something other than virtual currency, thetransfer itself may still be treated as a transfer of virtual currency,having value that depends on many potential factors including the valueof the item nominally transferred and the monetary value attendant tohaving the output of the transfer moved into a particular user'scontrol. The item of value may be associated with a digitally signedassertion 204 by means of an exterior protocol, such as the COLOREDCOINS created according to protocols developed by The Colored CoinsFoundation, the MASTERCOIN protocol developed by the MastercoinFoundation, or the ETHEREUM platform offered by the Stiftung EthereumFoundation of Baar, Switzerland, the Thunder protocol developed byThunder Consensus, or any other protocol.

Still referring to FIG. 2 , in one embodiment, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue in a digitally signed assertion 204. In some embodiments, addressis linked to a public key, the corresponding private key of which isowned by the recipient of a digitally signed assertion 204. Forinstance, address may be the public key. Address may be arepresentation, such as a hash, of the public key. Address may be linkedto the public key in memory of a computing device, for instance via a“wallet shortener” protocol. Where address is linked to a public key, atransferee in a digitally signed assertion 204 may record a subsequent adigitally signed assertion 204 transferring some or all of the valuetransferred in the first a digitally signed assertion 204 to a newaddress in the same manner. A digitally signed assertion 204 may containtextual information that is not a transfer of some item of value inaddition to, or as an alternative to, such a transfer. For instance, asdescribed in further detail below, a digitally signed assertion 204 mayindicate a confidence level associated with a distributed storage nodeas described in further detail below.

In an embodiment, and still referring to FIG. 2 immutable sequentiallisting 200 records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described herein,or may be readable and/or writable only by entities and/or deviceshaving such access privileges. Access privileges may exist in more thanone level, including, without limitation, a first access level orcommunity of permitted entities and/or devices having ability to read,and a second access level or community of permitted entities and/ordevices having ability to write; first and second community may beoverlapping or non-overlapping. In an embodiment, posted content and/orimmutable sequential listing 200 may be stored as one or more zeroknowledge sets (ZKS), Private Information Retrieval (PIR) structure, orany other structure that allows checking of membership in a set byquerying with specific properties. Such database may incorporateprotective measures to ensure that malicious actors may not query thedatabase repeatedly in an effort to narrow the members of a set toreveal uniquely identifying information of a given posted content.

Still referring to FIG. 2 , immutable sequential listing 200 maypreserve the order in which the at least a posted content took place bylisting them in chronological order; alternatively or additionally,immutable sequential listing 200 may organize digitally signedassertions 204 into sub-listings 208 such as “blocks” in a blockchain,which may be themselves collected in a temporally sequential order;digitally signed assertions 204 within a sub-listing 208 may or may notbe temporally sequential. The ledger may preserve the order in which atleast a posted content took place by listing them in sub-listings 208and placing the sub-listings 208 in chronological order. The immutablesequential listing 200 may be a distributed, consensus-based ledger,such as those operated according to the protocols promulgated by RippleLabs, Inc., of San Francisco, Calif., or the Stellar DevelopmentFoundation, of San Francisco, Calif., or of Thunder Consensus. In someembodiments, the ledger is a secured ledger; in one embodiment, asecured ledger is a ledger having safeguards against alteration byunauthorized parties. The ledger may be maintained by a proprietor, suchas a system administrator on a server, that controls access to theledger; for instance, the user account controls may allow contributorsto the ledger to add at least a posted content to the ledger, but maynot allow any users to alter at least a posted content that have beenadded to the ledger. In some embodiments, ledger is cryptographicallysecured; in one embodiment, a ledger is cryptographically secured whereeach link in the chain contains encrypted or hashed information thatmakes it practically infeasible to alter the ledger without betrayingthat alteration has taken place, for instance by requiring that anadministrator or other party sign new additions to the chain with adigital signature. Immutable sequential listing 200 may be incorporatedin, stored in, or incorporate, any suitable data structure, includingwithout limitation any database, datastore, file structure, distributedhash table, directed acyclic graph or the like. In some embodiments, thetimestamp of an entry is cryptographically secured and validated viatrusted time, either directly on the chain or indirectly by utilizing aseparate chain. In one embodiment the validity of timestamp is providedusing a time stamping authority as described in the RFC 3161 standardfor trusted timestamps, or in the ANSI ASC x9.95 standard. In anotherembodiment, the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 2 , immutablesequential listing 200, once formed, may be inalterable by any party, nomatter what access rights that party possesses. For instance, immutablesequential listing 200 may include a hash chain, in which data is addedduring a successive hashing process to ensure non-repudiation. Immutablesequential listing 200 may include a block chain. In one embodiment, ablock chain is immutable sequential listing 200 that records one or morenew at least a posted content in a data item known as a sub-listing 208or “block.” An example of a block chain is the BITCOIN block chain usedto record BITCOIN transactions and values. Sub-listings 208 may becreated in a way that places the sub-listings 208 in chronological orderand link each sub-listing 208 to a previous sub-listing 208 in thechronological order so that any computing device may traverse thesub-listings 208 in reverse chronological order to verify any at least aposted content listed in the block chain. Each new sub-listing 208 maybe required to contain a cryptographic hash describing the previoussub-listing 208. In some embodiments, the block chain contains a singlefirst sub-listing 208 sometimes known as a “genesis block.”

Still referring to FIG. 2 , the creation of a new sub-listing 208 may becomputationally expensive; for instance, the creation of a newsub-listing 208 may be designed by a “proof of work” protocol acceptedby all participants in forming the immutable sequential listing 200 totake a powerful set of computing devices a certain period of time toproduce. Where one sub-listing 208 takes less time for a given set ofcomputing devices to produce the sub-listing 208 protocol may adjust thealgorithm to produce the next sub-listing 208 so that it will requiremore steps; where one sub-listing 208 takes more time for a given set ofcomputing devices to produce the sub-listing 208 protocol may adjust thealgorithm to produce the next sub-listing 208 so that it will requirefewer steps. As an example, protocol may require a new sub-listing 208to contain a cryptographic hash describing its contents; thecryptographic hash may be required to satisfy a mathematical condition,achieved by having the sub-listing 208 contain a number, called a nonce,whose value is determined after the fact by the discovery of the hashthat satisfies the mathematical condition. Continuing the example, theprotocol may be able to adjust the mathematical condition so that thediscovery of the hash describing a sub-listing 208 and satisfying themathematical condition requires more or less steps, depending on theoutcome of the previous hashing attempt. Mathematical condition, as anexample, might be that the hash contains a certain number of leadingzeros and a hashing algorithm that requires more steps to find a hashcontaining a greater number of leading zeros, and fewer steps to find ahash containing a lesser number of leading zeros. In some embodiments,production of a new sub-listing 208 according to the protocol is knownas “mining.” The creation of a new sub-listing 208 may be designed by a“proof of stake” protocol as will be apparent to those skilled in theart upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 2 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 208. The incentive may befinancial; for instance, successfully mining a new sub-listing 208 mayresult in the person or entity that mines the sub-listing 208 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 208. Each sub-listing 208 createdin immutable sequential listing 200 may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 208.

With continued reference to FIG. 2 , where two entities simultaneouslycreate new sub-listings 208, immutable sequential listing 200 maydevelop a fork; protocol may determine which of the two alternatebranches in the fork is the valid new portion of the immutablesequential listing 200 by evaluating, after a certain amount of time haspassed, which branch is longer. “Length” may be measured according tothe number of sub-listings 208 in the branch. Length may be measuredaccording to the total computational cost of producing the branch.Protocol may treat only at least a posted content contained the validbranch as valid at least a posted content. When a branch is foundinvalid according to this protocol, at least a posted content registeredin that branch may be recreated in a new sub-listing 208 in the validbranch; the protocol may reject “double spending” at least a postedcontent that transfer the same virtual currency that another at least aposted content in the valid branch has already transferred. As a result,in some embodiments the creation of fraudulent at least a posted contentrequires the creation of a longer immutable sequential listing 200branch by the entity attempting the fraudulent at least a posted contentthan the branch being produced by the rest of the participants; as longas the entity creating the fraudulent at least a posted content islikely the only one with the incentive to create the branch containingthe fraudulent at least a posted content, the computational cost of thecreation of that branch may be practically infeasible, guaranteeing thevalidity of all at least a posted content in the immutable sequentiallisting 200.

Still referring to FIG. 2 , additional data linked to at least a postedcontent may be incorporated in sub-listings 208 in the immutablesequential listing 200; for instance, data may be incorporated in one ormore fields recognized by block chain protocols that permit a person orcomputer forming a at least a posted content to insert additional datain the immutable sequential listing 200. In some embodiments, additionaldata is incorporated in an unspendable at least a posted content field.For instance, the data may be incorporated in an OP RETURN within theBITCOIN block chain. In other embodiments, additional data isincorporated in one signature of a multi-signature at least a postedcontent. In an embodiment, a multi-signature at least a posted contentis at least a posted content to two or more addresses. In someembodiments, the two or more addresses are hashed together to form asingle address, which is signed in the digital signature of the at leasta posted content. In other embodiments, the two or more addresses areconcatenated. In some embodiments, two or more addresses may be combinedby a more complicated process, such as the creation of a Merkle tree orthe like. In some embodiments, one or more addresses incorporated in themulti-signature at least a posted content are typical crypto-currencyaddresses, such as addresses linked to public keys as described above,while one or more additional addresses in the multi-signature at least aposted content contain additional data related to the at least a postedcontent; for instance, the additional data may indicate the purpose ofthe at least a posted content, aside from an exchange of virtualcurrency, such as the item for which the virtual currency was exchanged.In some embodiments, additional information may include networkstatistics for a given node of network, such as a distributed storagenode, e.g. the latencies to nearest neighbors in a network graph, theidentities or identifying information of neighboring nodes in thenetwork graph, the trust level and/or mechanisms of trust (e.g.certificates of physical encryption keys, certificates of softwareencryption keys, (in non-limiting example certificates of softwareencryption may indicate the firmware version, manufacturer, hardwareversion and the like), certificates from a trusted third party,certificates from a decentralized anonymous authentication procedure,and other information quantifying the trusted status of the distributedstorage node) of neighboring nodes in the network graph, IP addresses,GPS coordinates, and other information informing location of the nodeand/or neighboring nodes, geographically and/or within the networkgraph. In some embodiments, additional information may include historyand/or statistics of neighboring nodes with which the node hasinteracted. In some embodiments, this additional information may beencoded directly, via a hash, hash tree or other encoding.

With continued reference to FIG. 2 , in some embodiments, virtualcurrency is traded as a crypto-currency. In one embodiment, acrypto-currency is a digital, currency such as Bitcoins, Peercoins,Namecoins, and Litecoins. Crypto-currency may be a clone of anothercrypto-currency. The crypto-currency may be an “alt-coin.”Crypto-currency may be decentralized, with no particular entitycontrolling it; the integrity of the crypto-currency may be maintainedby adherence by its participants to established protocols for exchangeand for production of new currency, which may be enforced by softwareimplementing the crypto-currency. Crypto-currency may be centralized,with its protocols enforced or hosted by a particular entity. Forinstance, crypto-currency may be maintained in a centralized ledger, asin the case of the XRP currency of Ripple Labs, Inc., of San Francisco,Calif. In lieu of a centrally controlling authority, such as a nationalbank, to manage currency values, the number of units of a particularcrypto-currency may be limited; the rate at which units ofcrypto-currency enter the market may be managed by a mutuallyagreed-upon process, such as creating new units of currency whenmathematical puzzles are solved, the degree of difficulty of the puzzlesbeing adjustable to control the rate at which new units enter themarket. Mathematical puzzles may be the same as the algorithms used tomake productions of sub-listings 208 in a block chain computationallychallenging; the incentive for producing sub-listings 208 may includethe grant of new crypto-currency to the miners. Quantities ofcrypto-currency may be exchanged using at least a posted content asdescribed above.

Referring to FIG. 3 , an exemplary embodiment of fuzzy set comparison300 is illustrated. A first fuzzy set 304 may be represented, withoutlimitation, according to a first membership function 308 representing aprobability that an input falling on a first range of values 312 is amember of the first fuzzy set 304, where the first membership function308 has values on a range of probabilities such as without limitationthe interval [0,1], and an area beneath the first membership function308 may represent a set of values within first fuzzy set 304. Althoughfirst range of values 312 is illustrated for clarity in this exemplarydepiction as a range on a single number line or axis, first range ofvalues 312 may be defined on two or more dimensions, representing, forinstance, a Cartesian product between a plurality of ranges, curves,axes, spaces, dimensions, or the like. First membership function 308 mayinclude any suitable function mapping first range 312 to a probabilityinterval, including without limitation a triangular function defined bytwo linear elements such as line segments or planes that intersect at orbelow the top of the probability interval. As a non-limiting example,triangular membership function may be defined as:

${y( {x,a,b,c} )} = \{ \begin{matrix}{0,{{{for}x} > {c{and}x} < a}} \\{\frac{x - a}{b - a},{{{for}a} \leq x < b}} \\{\frac{c - x}{c - b},{{{if}b} < x \leq c}}\end{matrix} $

a trapezoidal membership function may be defined as:

${y( {x,a,b,c,d} )} = {\max( {{\min( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} )},0} )}$

a sigmoidal function may be defined as:

${y( {x,a,c} )} = \frac{1}{1 - e^{- {a({x - c})}}}$

a Gaussian membership function may be defined as:

${y( {x,c,\sigma} )} = e^{- \frac{1}{2}{(\frac{x - c}{\sigma})}^{2}}$

and a bell membership function may be defined as:

${y( {x,a,b,c,} )} = \lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \rbrack^{- 1}$

Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 3 , first fuzzy set 304 may represent any valueor combination of values as described above, including output from oneor more machine-learning models, resource enhancement probabilities, anda predetermined class, such as without limitation of resourceenhancement processes. A second fuzzy set 316, which may represent anyvalue which may be represented by first fuzzy set 304, may be defined bya second membership function 320 on a second range 324; second range 324may be identical and/or overlap with first range 312 and/or may becombined with first range via Cartesian product or the like to generatea mapping permitting evaluation overlap of first fuzzy set 304 andsecond fuzzy set 316. Where first fuzzy set 304 and second fuzzy set 316have a region 328 that overlaps, first membership function 308 andsecond membership function 320 may intersect at a point 332 representinga probability, as defined on probability interval, of a match betweenfirst fuzzy set 304 and second fuzzy set 316. Alternatively oradditionally, a single value of first and/or second fuzzy set may belocated at a locus 336 on first range 312 and/or second range 324, wherea probability of membership may be taken by evaluation of firstmembership function 308 and/or second membership function 320 at thatrange point. A probability at 328 and/or 332 may be compared to athreshold 340 to determine whether a positive match is indicated.Threshold 340 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 304 and second fuzzy set 316, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and/or resource enhancementprobabilities and a predetermined class, such as without limitationresource enhancement processes categorization, for combination to occuras described above. Alternatively or additionally, each threshold may betuned by a machine-learning and/or statistical process, for instance andwithout limitation as described in further detail below.

Further referring to FIG. 3 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify resource enhancementprobabilities with resource enhancement processes. For instance, if aresource enhancement process has a fuzzy set matching a resourceenhancement probability fuzzy set by having a degree of overlapexceeding a threshold, apparatus 100 may classify the resourceenhancement probability as belonging to the resource enhancement processcategorization. Where multiple fuzzy matches are performed, degrees ofmatch for each respective fuzzy set may be computed and aggregatedthrough, for instance, addition, averaging, or the like, to determine anoverall degree of match.

Still referring to FIG. 3 , in an embodiment, a resource enhancementprobability may be compared to multiple resource enhancement processcategorization fuzzy sets. For instance, resource enhancementprobabilities may be represented by a fuzzy set that is compared to eachof the multiple resource enhancement process categorization fuzzy sets;and a degree of overlap exceeding a threshold between the resourceenhancement probabilities fuzzy set and any of the multiple resourceenhancement processes categorization fuzzy sets may cause apparatus 100to classify the resource enhancement probabilities as belonging to theresource enhancement processes categorization. For instance, in oneembodiment there may be two resource enhancement processescategorization fuzzy sets, representing respectively a first resourceenhancement processes categorization and a second resource enhancementprocesses categorization. First resource enhancement processescategorization may have a first fuzzy set; Second resource enhancementprocesses categorization may have a second fuzzy set; and resourceenhancement probabilities may have a resource enhancement probabilitiesfuzzy set. Apparatus 100, for example, may compare resource enhancementprobabilities fuzzy set with each of resource enhancement processescategorization fuzzy set and in a resource enhancement processescategorization fuzzy set, as described above, and classify resourceenhancement probabilities to either, both, or neither of resourceenhancement processes categorization or in a resource enhancementprocesses categorization. Machine-learning methods as describedthroughout may, in a non-limiting example, generate coefficients used infuzzy set equations as described above, such as without limitation x, c,and a of a Gaussian set as described above, as outputs ofmachine-learning methods. Likewise, resource enhancement probabilitiesmay be used indirectly to determine a fuzzy set, as resource enhancementmetrics fuzzy set may be derived from outputs of one or moremachine-learning models that take the resource enhancement probabilitiesdirectly or indirectly as inputs.

Still referring to FIG. 3 , a computing device and/or apparatus 100 mayuse a logic comparison program, such as, but not limited to, a fuzzylogic model to determine a resource enhancement process risk factor. Aresource enhancement process risk factor may include, but is not limitedto, low risk, medium risk, high risk, and the like; each such resourceenhancement process risk factor may be represented as a value for alinguistic variable representing resource enhancement processes riskfactors or in other words a fuzzy set as described above thatcorresponds to a degree of loss probability as calculated using anystatistical, machine-learning, or other method that may occur to aperson skilled in the art upon reviewing the entirety of thisdisclosure. In other words, a given element of resource enhancementmetrics may have a first non-zero value for membership in a firstlinguistic variable value such as “10” and a second non-zero value formembership in a second linguistic variable value such as “5” In someembodiments, determining a resource enhancement processes categorizationmay include using a linear regression model. A linear regression modelmay include a machine learning model. A linear regression model may beconfigured to map data of resource enhancement probabilities, such asdegree of risk to one or more resource enhancement processes parameters.A linear regression model may be trained using a machine learningprocess. A linear regression model may map statistics such as, but notlimited to, quality of resource enhancement probabilities, lossprobabilities of assets, and the like. In some embodiments, determininga resource enhancement process of resource enhancement probabilities mayinclude using a resource enhancement processes classification model. Aresource enhancement process classification model may be configured toinput collected data and cluster data to a centroid based on, but notlimited to, frequency of appearance, linguistic indicators of quality,and the like. Centroids may include scores assigned to them such thatquality of resource enhancement processes of resource enhancementmetrics may each be assigned a score. In some embodiments resourceenhancement processes classification model may include a K-meansclustering model. In some embodiments, resource enhancement processesclassification model may include a particle swarm optimization model. Insome embodiments, determining the resource enhancement processes ofresource enhancement probabilities may include using a fuzzy inferenceengine. A fuzzy inference engine may be configured to map one or moreresource enhancement probabilities data elements using fuzzy logic. Insome embodiments, resource enhancement probabilities may be arranged bya logic comparison program into resource enhancement probabilitiesarrangements. A “resource enhancement probabilities arrangement” as usedin this disclosure is any grouping of objects and/or data based on lossprobabilities, risk level and/or predicted increases in asset value.This step may be implemented, without limitation, as described above inFIG. 1 . Membership function coefficients and/or constants as describedabove may be tuned according to classification and/or clusteringalgorithms. For instance, and without limitation, a clustering algorithmmay determine a Gaussian or other distribution of resource variabilitiesabout a centroid corresponding to a given risk level, and an iterativeor other method may be used to find a membership function, for anymembership function type as described above, that minimizes an averageerror from the statistically determined distribution, such that, forinstance, a triangular or Gaussian membership function about a centroidrepresenting a center of the distribution that most closely matches thedistribution. Error functions to be minimized, and/or methods ofminimization, may be performed without limitation according to any errorfunction and/or error function minimization process and/or method asdescribed in this disclosure.

Further referring to FIG. 3 , an inference engine may be implementedaccording to input and/or output membership functions and/or linguisticvariables. For instance, a first linguistic variable may represent afirst measurable value pertaining to resource enhancement probabilities,such as a degree of weighted value of a resource, while a secondmembership function may indicate a degree of resource enhancementprocesses of a subject thereof, or another measurable value pertainingto resource enhancement probabilities. Continuing the example, an outputlinguistic variable may represent, without limitation, a score value. Aninference engine may combine rules, such as: “if the resourcevariability is ‘high’ and the loss probability is ‘high’ the resourceenhancement process is ‘high risk’”—the degree to which a given inputfunction membership matches a given rule may be determined by atriangular norm or “T-norm” of the rule or output membership functionwith the input membership function, such as min (a, b), product of a andb, drastic product of a and b, Hamacher product of a and b, or the like,satisfying the rules of commutativity (T(a, b)=T(b, a)), monotonicity:(T(a, b)≤T(c, d) if a≤c and b≤d), (associativity: T(a, T(b, c))=T(T(a,b), c)), and the requirement that the number 1 acts as an identityelement. Combinations of rules (“and” or “or” combination of rulemembership determinations) may be performed using any T-conorm, asrepresented by an inverted T symbol or “⊥” such as max(a, b),probabilistic sum of a and b (a+b−a*b), bounded sum, and/or drasticT-conorm; any T-conorm may be used that satisfies the properties ofcommutativity: ⊥(a, b)=⊥(b, a), monotonicity: ⊥(a, b)≤⊥(c, d) if a≤c andb≤d, associativity: ⊥(a, ⊥(b, c))=⊥(⊥(a, b), c), and identity element of0. Alternatively or additionally T-conorm may be approximated by sum, asin a “product-sum” inference engine in which T-norm is product andT-conorm is sum. A final output score or other fuzzy inference outputmay be determined from an output membership function as described aboveusing any suitable defuzzification process, including without limitationMean of Max defuzzification, Centroid of Area/Center of Gravitydefuzzification, Center Average defuzzification, Bisector of Areadefuzzification, or the like. Alternatively or additionally, outputrules may be replaced with functions according to the Takagi-Sugeno-King(TSK) fuzzy model.

Further referring to FIG. 3 , resource enhancement probabilities to beused may be selected by user selection, and/or by selection of adistribution of output scores, such as 40% low risk, 40% moderate risk,and 20% high risk or the like. Each resource enhancement processescategorization may be selected using an additional function such as in aresource enhancement process as described above.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 408 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample inputs may include resource data and outputs may includeresource enhancement probabilities.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data to resourcecategories, such as, but not limited to, unused, used, asset increaser,asset decreaser, and the like.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude resource data as described above as inputs, resource processesas outputs, and a scoring function representing a desired form ofrelationship to be detected between inputs and outputs; scoring functionmay, for instance, seek to maximize the probability that a given inputand/or combination of elements inputs is associated with a given outputto minimize the probability that a given input is not associated with agiven output. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in training data 404.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of at least asupervised machine-learning process 428 that may be used to determinerelation between inputs and outputs. Supervised machine-learningprocesses may include classification algorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminant analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized trees, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 5 , a method 500 of generating a resourceenhancement probability model is presented. At step 505, method 500includes receiving resource data. Resource data may be revied through ahost platform, API, user input, and/or other communications, withoutlimitation. Resource data may include data of a user's financialaccounts, asset values, and the like. This step may be implemented,without limitation, as described above in FIGS. 1-4 .

Still referring to FIG. 5 , at step 505, method 500 includes comparingresource data to a resource enhancement metric. Resource data may becompared to a resource enhancement metric through an optimizationproblem, machine learning model, and the like. This step may beimplemented, without limitation, as described above in FIGS. 1-4 .

Still referring to FIG. 5 , at step 510, method 500 includes generatinga resource enhancement probability model. A resource enhancementprobability model may include any machine learning model as describedthroughout this disclosure, without limitation. In some embodiments, aresource enhancement probability model may be generated through trainingdata. Training data may include resource data correlated to resourceenhancement probabilities. Training data may be received through userinput, external computing devices, and/or previous iterations ofprocessing, without limitation. A resource enhancement probability modelmay be configured to generate a resource variability. A resourcevariability may include a predicted increase or decrease of value of oneor more assets of a user's resource. In some embodiments, calculating aresource variability may include determining a temporal element of theresource data. In some embodiments, a resource variability may include astatic resource behavior. This step may be implemented, withoutlimitation, as described above in FIGS. 1-4 .

Still referring to FIG. 5 , at step 515, method 500 includes calculatinga resource enhancement probability. A resource enhancement probabilitymay include a risk associated with reallocating one or more assets. Insome embodiments, a resource enhancement probability may be used tocalculate a resource enhancement process. A resource enhancement processmay be executed by a host platform, apparatus 100, and/or othercomputing device. A resource enhancement process may includereallocating one or more assets of a financial account to a cryptowallet. In some embodiments, a resource enhancement process may beprovided to a user through a graphical user interface (GUI). This stepmay be implemented, without limitation, as described above in FIGS. 1-4.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Still referring to FIG. 6 , processor 604 may include any suitableprocessor, such as without limitation a processor incorporating logicalcircuitry for performing arithmetic and logical operations, such as anarithmetic and logic unit (ALU), which may be regulated with a statemachine and directed by operational inputs from memory and/or sensors;processor 604 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Processor 604 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC).

Still referring to FIG. 6 , memory 608 may include various components(e.g., machine-readable media) including, but not limited to, arandom-access memory component, a read only component, and anycombinations thereof. In one example, a basic input/output system 616(BIOS), including basic routines that help to transfer informationbetween elements within computer system 600, such as during start-up,may be stored in memory 608. Memory 608 may also include (e.g., storedon one or more machine-readable media) instructions (e.g., software) 620embodying any one or more of the aspects and/or methodologies of thepresent disclosure. In another example, memory 608 may further includeany number of program modules including, but not limited to, anoperating system, one or more application programs, other programmodules, program data, and any combinations thereof.

Still referring to FIG. 6 , computer system 600 may also include astorage device 624. Examples of a storage device (e.g., storage device624) include, but are not limited to, a hard disk drive, a magnetic diskdrive, an optical disc drive in combination with an optical medium, asolid-state memory device, and any combinations thereof. Storage device624 may be connected to bus 612 by an appropriate interface (not shown).Example interfaces include, but are not limited to, SCSI, advancedtechnology attachment (ATA), serial ATA, universal serial bus (USB),IEEE 1394 (FIREWIRE), and any combinations thereof. In one example,storage device 624 (or one or more components thereof) may be removablyinterfaced with computer system 600 (e.g., via an external portconnector (not shown)). Particularly, storage device 624 and anassociated machine-readable medium 628 may provide nonvolatile and/orvolatile storage of machine-readable instructions, data structures,program modules, and/or other data for computer system 600. In oneexample, software 620 may reside, completely or partially, withinmachine-readable medium 628. In another example, software 620 mayreside, completely or partially, within processor 604.

Still referring to FIG. 6 , computer system 600 may also include aninput device 632. In one example, a user of computer system 600 mayenter commands and/or other information into computer system 600 viainput device 632. Examples of an input device 632 include, but are notlimited to, an alpha-numeric input device (e.g., a keyboard), a pointingdevice, a joystick, a gamepad, an audio input device (e.g., amicrophone, a voice response system, etc.), a cursor control device(e.g., a mouse), a touchpad, an optical scanner, a video capture device(e.g., a still camera, a video camera), a touchscreen, and anycombinations thereof. Input device 632 may be interfaced to bus 612 viaany of a variety of interfaces (not shown) including, but not limitedto, a serial interface, a parallel interface, a game port, a USBinterface, a FIREWIRE interface, a direct interface to bus 612, and anycombinations thereof. Input device 632 may include a touch screeninterface that may be a part of or separate from display 636, discussedfurther below. Input device 632 may be utilized as a user selectiondevice for selecting one or more graphical representations in agraphical interface as described above.

Still referring to FIG. 6 , a user may also input commands and/or otherinformation to computer system 600 via storage device 624 (e.g., aremovable disk drive, a flash drive, etc.) and/or network interfacedevice 640. A network interface device, such as network interface device640, may be utilized for connecting computer system 600 to one or moreof a variety of networks, such as network 644, and one or more remotedevices 648 connected thereto. Examples of a network interface deviceinclude, but are not limited to, a network interface card (e.g., amobile network interface card, a LAN card), a modem, and any combinationthereof. Examples of a network include, but are not limited to, a widearea network (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 644, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 620, etc.) may be communicated to and/or fromcomputer system 600 via network interface device 640.

Still referring to FIG. 6 , computer system 600 may further include avideo display adapter 652 for communicating a displayable image to adisplay device, such as display device 636. Examples of a display deviceinclude, but are not limited to, a liquid crystal display (LCD), acathode ray tube (CRT), a plasma display, a light emitting diode (LED)display, and any combinations thereof. Display adapter 652 and displaydevice 636 may be utilized in combination with processor 604 to providegraphical representations of aspects of the present disclosure. Inaddition to a display device, computer system 600 may include one ormore other peripheral output devices including, but not limited to, anaudio speaker, a printer, and any combinations thereof. Such peripheraloutput devices may be connected to bus 612 via a peripheral interface656. Examples of a peripheral interface include, but are not limited to,a serial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,apparatuses, systems, and software according to the present disclosure.Accordingly, this description is meant to be taken only by way ofexample, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for generating a resourceenhancement probability model, comprising: at least a processor; and amemory communicatively connected to the at least a processor, the memorycontaining instructions configuring the at least a processor to: receiveresource data; compare the resource data to a resource enhancementmetric; generate, as a function of the comparison, a resourceenhancement probability model; and calculate, as a function of theresource enhancement probability model, a resource enhancementprobability.
 2. The apparatus of claim 1, wherein the memory containsinstructions further configuring the at least a processor to execute aresource enhancement process as a function of the resource enhancementprobability.
 3. The apparatus of claim 1, wherein generating theresource enhancement probability model comprises: receiving trainingdata correlating resource data to resource enhancement probabilities;training a resource enhancement probability model with the trainingdata; and generating a resource enhancement probability as a function ofthe resource enhancement probability model, wherein the resourceenhancement probability model is configured to input resource data andoutput resource enhancement probabilities.
 4. The apparatus of claim 1,wherein the memory contains instructions further configuring the atleast a processor to: determine a temporal element of the resourceenhancement probability; and execute a resource enhancement process as afunction of the temporal element of the resource enhancementprobability.
 5. The apparatus of claim 1, wherein the memory containsinstructions further configuring the at least a processor to generate ahost platform that is configured to communicate with a plurality ofcomputing devices through an application programming interface (API). 6.The apparatus of claim 1, wherein the memory contains instructionsfurther configuring the at least a processor to determine a quantity ofstatic resources of the resource data as a function of the resourceenhancement metric.
 7. The apparatus of claim 1, wherein the memorycontains instructions further configuring the at least a processor tocalculate a resource variability using a resource variability machinelearning model, wherein the resource variability machine learning modelis configured to input resource data and output a plurality of lossprobabilities of assets.
 8. The apparatus of claim 1, wherein theresource enhancement probability comprises allocating the user's assetsusing an immutable sequential listing.
 9. The apparatus of claim 1,wherein the memory contains instructions further configuring the atleast a processor to: determine a user pattern of the resource data; andgenerate a resource enhancement probability as a function of the userpattern.
 10. The apparatus of claim 1, wherein the memory containsinstructions further configuring the at least a processor to display theresource enhancement probability through a graphical user interface(GUI).
 11. A method of generating a resource enhancement probabilitymodel using a computing device, comprising: receiving resource data at acomputing device; comparing, at the computing device, the resource datato a resource enhancement metric; generating, as a function of thecomparison, a resource enhancement probability model; and calculating,at the computing device, as a function of the resource enhancementprobability model, a resource enhancement probability.
 12. The method ofclaim 11, further comprising executing a resource enhancement process asa function of the resource enhancement probability.
 13. The method ofclaim 11, wherein generating the resource enhancement probability modelfurther comprises: receiving training data correlating resource data toresource enhancement probabilities; training a resource enhancementprobability model with the training data; and generating a resourceenhancement probability as a function of the resource enhancementprobability model, wherein the resource enhancement probability model isconfigured to input resource data and output resource enhancementprobabilities.
 14. The method of claim 11, wherein calculating theresource enhancement probability further comprises: determining atemporal element of the resource enhancement probability; and executinga resource enhancement process as a function of the temporal element ofthe resource enhancement probability.
 15. The method of claim 11,wherein receiving resource data further comprises generating a hostplatform that is configured to communicate with a plurality of computingdevices through an application programming interface (API).
 16. Themethod of claim 11, further comprising determining a quantity of staticresources of the resource data as a function of the resource enhancementmetric.
 17. The method of claim 11, wherein calculating the resourceenhancement probability further comprises calculating a resourcevariability using a resource variability machine learning model, whereinthe resource variability machine learning model is configured to inputresource data and output a plurality of loss probabilities of assets.18. The method of claim 11, wherein the resource enhancement probabilitycomprises allocating the user's assets using an immutable sequentiallisting.
 19. The method of claim 11, further comprising: determining auser pattern of the resource data; and generating a resource enhancementprobability as a function of the user pattern.
 20. The method of claim11, further comprising displaying the resource enhancement probabilitythrough a graphical user interface (GUI).